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Abstract: With the development of self-driving technology in automotive industry, transportation safety has a very close connection with AI. There are two main approaches for the AI applications for traffic accidents. One is the analysis and solving of the potential risk of traffic accidents. Another is maximizing the safety of people’s lives if the accident cannot be avoided. Both of the works act as an important part to prevent traffic accidents. They require applications’ analysis and making decisions within a short time. This paper is introduced the methods for AI dealing with traffic problems, which is the most important part of applications’ processing. Optimizing the codes and decreasing the reaction time of the applications is also necessary to maximize saving people’s lives.
Keywords: recognize; process; reaction; training model; AI, traffic accidents, reaction time, detection, identification.
Since the first real self-driving car was manufactured in the 1980s, scientists have been always dedicating themselves to improving the ability of self-driving systems. With the quick development of embedded computers and artificial intelligence, in-car computers now are more likely to simulate driving scenarios and predict the potential risks during driving.
Additionally, according to the SAE International[i], six levels of driving automation were defined, from Level 0 to Level 5, which are gradually from limited driver support features to automatic driving under all conditions.
Additionally, according to NHTSA[ii], about 93% of traffic accident is caused by human beings[iii]. People are likely to distract while driving, but programs seem never to make mistakes. Therefore, in theory, self-driving systems can greatly reduce the frequency of accidents.
SAE J3016 Levels of driving automation graph
There are two main sources of risk to driving, environmental and human factors. Environmental factors are generated by the driving environment itself. For example, low visibility conditions due to foggy weather and icy roads make it difficult to get control of vehicles. Human factors are mainly non-compliance with traffic rules and unexpected actions from human beings. For example, irregular lane changes, not following the marked direction of travel and do not follow the instructions of the traffic lights. Under these all kinds of constructions, AI must find an appropriate way to avoid accidents happen or cause minimal casualties.
Traffic Accident on icy road[iv]
The whole system is made up of three main parts. The first part is detractors monitor the whole environment around the car. Cameras are used for the detection and identification of signs, markings on the ground, and tail light status of the vehicle in the front, etc. LIDARs are used for detecting the distance of obstacles ahead and the objects around the cars. Ultrasonic Radars are used for detecting obstacles in low-speed scenarios, such as in the process of entering the vehicle into the parking space.
The second part is the embedded system analyzes the data collected by the sensors and calculates and identifies the potential risks in the future driving process, making plans to respond to these threats.
The third part is to mobilize the vehicle’s brakes, steering wheel and engine when necessary.
From the traditional man-drive car gradually intelligent is a long-term process. Currently, The highest level of autonomous driving that has been marketed is L2, which still required drivers put their hands on the steering wheels and only provide help in specific scenarios[v]. There is still a long way to go before people want to use AI to avoid traffic accidents. The road environment is highly variable, so AI needs to consider many factors to avoid accidents to the maximum extent possible.
At this stage, the ai safety system plays the role of a watchdog in driving. It automatically changes the vehicle’s trajectory and reduces speed as much as possible when pedestrians or obstacles appear in front of the vehicle. However, that requires plenty of calculation before the AI does these operations. Before turning the steering wheel, AI should consider if the speed is safe to change direction. Otherwise, there is a risk of the vehicle overturning or losing control. It should use the LIDARs to detect the distance to the vehicle behind the adjacent lane to prevent danger from the rear.
autonomous-cars-lidar-sensors [vi]
There are also extreme cases where a crash is unavoidable. At this point, AI should try to avoid injury to the greatest extent possible. AI can try to pop the airbag in advance, tighten the seat belt, and use the more protective parts of the vehicle for impact.
From the perspective of the historical development of technology, the car computer will have a great possibility to access the Internet in the form of the Internet of things. If that happened, cars can access and share real-time road information from a unified platform. Based on this information, the driver can anticipate possible accidents and risks ahead, optimize the driving route and alert the driver. In some unusual roads, AI can give some driving suggestions by analyzing the historical data of previous vehicles, such as braking early, changing lanes, watching out for cars at the entrance and exit of the road, etc. Most serious accidents are caused by the accumulation of multiple error factors, which may be avoided if any one factor is addressed in advance.
The driver’s factors are also an important cause of accidents, such as drunk driving, fatigue or extreme emotions. According to the NBSC[vii], in 2008 in China, the number of traffic accident deaths caused by drunk driving accounts for 4.16% of the total number of traffic accident deaths[viii]. Therefore, in addition to monitoring the state of the road, AI should also include the detection of the driver. In case of frequent abnormal driving conditions, such as multiple-lane departures, the driver should be advised to stop and rest. When road conditions allow and technology permits, autonomous driving services can be provided to reduce the burden and fatigue of drivers driving for long periods. When a driver is detected as a risk of driving under the influence of alcohol, drugs or substances, the driver should be discouraged and offered a viable solution by providing navigation planning for nearby rest stops or calling a chauffeur on his or her behalf. Automatically calls the driver’s family members or police for assistance when necessary.
According to the Volvo Trucks Safety Report 2017[ix], 1.2 million people are killed in road traffic accidents Worldwide each year. 60% of truck and pedestrian (or bicycle) collisions occur in urban areas. Therefore, it seems that traffic accidents are more likely to occur in cities. Although the speed in the city is slower, the driving scenario on the street is more complex and requires the driver to concentrate on various unexpected situations. In fact, there are more feasible solutions to traffic accidents in the city as well. The slower speed allows the vehicle to slow down faster, leaving the driver more time to react.
Volvo Trucks Safety Report In modern cities, traffic management usually sets up some monitoring and speed-measuring devices. It would be useful if these devices could be connected to AI systems for traffic accident prevention and avoidance.
The first step is to interconnect the in-vehicle system with the municipal information system. For a faster and more stable connection, this system is not necessarily connected to the server through the cellular mobile network as in traditional communication, but rather to the communication system on the road facilities nearby.
The in-vehicle system then exchanges information with the street facility. The onboard system sends information such as the car’s route and speed to the street traffic facility, and the street facility sends several surrounding intersections and street information to the car, such as speed information of vehicles ahead, and pedestrian information.
This can solve the problems caused by blind spots and narrow roads in streets with complex driving conditions, and also includes pedestrians and non-motorized drivers into the consideration of the entire system, not just other motor vehicles.
Meanwhile, in order to save the computing resources of the car AI system, the above-mentioned computing part should be performed by the ground computing system as much as possible and send the calculation results directly to the car, which will then decide the next operation and send its decision to the street system again for reference by other vehicles. Also, reminder signs can be set up on the streets, such as on some narrow streets, to alert other pedestrians and non-motorized vehicles when a vehicle is passing.
Large trucks cause an important part of the accident. According to the NHTSA “Traffic Safety Facts 2014[x]”, In terms of crash rates, large trucks are higher than family buses. The fatal crash rate for buses in 2014 was 1.34 compared to 1.29 for family buses, with family buses having a lower accident rate than large trucks.
Compared with ordinary cars, trucks have more volume and mass, which makes them more difficult to maneuver, braking and turning takes longer. This is exactly why they cause more accidents. In the prevention of such accidents, we should give higher priority to these large vehicles and give more to smaller vehicles to avoid these accidents. When they can’t stop, they can send a signal to the vehicle in front of them, making the AI in front of them to avoid tailgating accidents automatically. Such messages will not be sent to a specific vehicle, but will be broadcast to all cars in the surrounding area without discrimination.
In the above paragraphs, some ways to avoid accidents are explained. However, in some cases, collisions and injuries are always unavoidable. At this point we should seek some options to minimize injuries.
In the current design scheme, the more common method of reducing damage is mainly through strengthening the body structure, equipped with occupant protection devices (such as airbags) to achieve this purpose. Most of these protective measures are reactive and therefore have many limitations. There will always be some special circumstances that prevent the protection from taking effect. AI can protect passengers by intervening proactively.
The impact force from the collision is the main cause of the injury. If the AI can reduce the impact, the injury can be reduced. When impacting, try to avoid bulky vehicles or hard objects, and absorb energy by hitting barriers that can provide a cushion such as guardrails and green belts. And use the angle of the vehicle with better protection to face the obstacle, avoid contacting the obstacle from the side and offset the angle. The AI can analyze material information from the vehicle ahead as well as from surrounding obstacles. Avoid collision with trucks, buses, etc. transporting hazardous materials and choose grass and woods instead.
Side Mobile Barrier test[xi]
In high-speed driving, if you detect too much speed to avoid a crash, you can first tighten the passenger seat belt, lower the windows to prevent injuries from glass shards, and issue a warning alert to alert rear passengers. Buy them a few seconds to prepare for the impact in advance and use some collision avoidance positions to avoid injury.
After an accident, the AI should proactively call the rescue service and the driver’s family members, send the vehicle’s location, and provide, via the cloud platform, the damage to the vehicle, the injuries of the members, and the health information of the accompanying members saved in advance: such as age, blood type, drug allergy information, and historical diseases. In addition, it automatically turns on the vehicle’s double flashes and automatically uses the vehicle’s horn system to draw the attention of people passing around and seek help.
All of the above calculations require the AI to have a correct estimate of the vehicle’s state, weight, performance and other data. This usually takes a long time to test and learn before it can be used in real-life. This poses a great challenge to the learning ability of the AI, as each model has different performance conditions and different models of vehicles require targeted learning, testing and optimization.
AI can record drivers’ driving habits to improve prediction accuracy. The driver information generated by long time accumulation should be exportable and can be migrated and used synchronously on AI systems of the same architecture. Vehicle manufacturers should create a neutral organization to share information, share road information with the user’s consent, and make it easier for users to use their own personalized profiles across vehicles.
In addition to local-based computing, it is also possible to try to establish connections between vehicles using communication devices, allowing the AI to pass information and cooperate better in case of danger. During normal driving, AI in neighbor cars can use the peer-to-peer networking to communicate with each other about planned routes, so that potential risks can be detected in advance and accidents can be stopped.
Some of the ideas proposed above about AI applications to prevent traffic accidents are in the process of human practice, and some designs are still in the imagination stage. AI is the product of model training, so people still need more cases and experiences to optimize AI. At the same time, communication between vehicles requires close cooperation between vehicle manufacturers, and the system will be extremely useful when a certain percentage of vehicles can communicate on the road. In this regard, mankind still has a long way to go.
[i] Society of Automotive Engineers [ii] National Highway Traffic Safety Administration (US) [iii] https://one.nhtsa.gov/people/injury/research/udashortrpt/background.html [iv] https://www.cbc.ca/news/canada/manitoba/rutted-icy-roads-crashes-winnipeg-1.6309643 [v] https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic [vi] https://www.technologyreview.com/2017/03/20/153129/autonomous-cars-lidar-sensors/ [vii] National Bureau of Statistics of China [viii]https://transport.ckcest.cn/CatsCategory/listGLJCSJ3?tableName=cats_highwayac_reason&pubflag=1&code=C09&pageNo=1 [ix] https://www.volvogroup.com/en/about-us/traffic-safety/most-common-accidents.html [x] https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812261 [xi]https://www.euroncap.com/en/vehicle-safety/the-ratings-explained/adult-occupant-protection/lateral-impact/side-mobile-barrier/